Customized multi-agentic AI workflows made simple
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Agentic AI and multi-agent AI systems making autonomous, intelligent decisions for organizations are set to transform business for years to come

Agentic AI is defined by its ability to pursue multi-step goals, reason across complex, interconnected tasks, and act with autonomy. Unlike traditional AI, which is typically limited to single tasks and waits for explicit instructions, agentic AI operates with minimal human input and can make decisions independently. This capacity for autonomy is the key differentiator of agentic AI.

Agentic AI workflows and multi-agent AI systems manage complexity by coordinating across tasks, learning from outcomes, and refining their behavior over time. Importantly, they do so in dynamic environments, where conditions change quickly and decisions must be made in real time.

The agentic AI market is projected to leap from $5.1 billion in 2024 to $52.6 billion by 2030¹. This growing momentum is due to the clear value agentic AI workflows deliver across sectors.

Here are some of the use cases driving its adoption: 

How can businesses implement agentic AI? 

Organizations are eager to design and deploy agentic networks and reap the early benefits, yet are challenged by the complexity of data ecosystems, siloed enterprise systems, governance and scalability concerns, and the required technical overhead to deploy the agentic systems.  

To help organizations realize the benefits of agentic workflows, Capgemini has launched a no-code agentic self-service tool for experimenting and scaling multi-agent AI systems and agentic workflows. 

Part of a new generation of AI solutions, the agentic self-service tool is a platform that allows non-technical people to create AI agents, execute multi-agent workflows, and leverage the environment to create agent-driven business case.  

The agentic self-service tool brings together the power of hybrid human-AI teams to achieve agentic-driven business transformation with unprecedented speed and efficiency. 

The tool is designed to be simple enough for users to create agents independently. 

Building effective AI agents 

When we talk about an AI agent, we are speaking about a system that is able to perform tasks on your behalf. So, the first step in creating an AI agent is to understand its purpose. Once you have decided on the purpose of the agent, you can define the agent’s role. For instance, in knowledge work, you might want to create a summary agent that can read and summarize documents. 

Foundational models that deliver on performance and cost 

Central to an AI agent’s operation is the foundational model it uses. A foundational model processes and generates human-like text based on the input it receives. It can understand context, interpret complex instructions, answer questions, provide recommendations, and operate flexibly in unpredictable real-world scenarios. 

The agentic self-service tool uses a default foundational model optimized for performance and cost. However, the platform provides the flexibility for you to choose alternative models that are more suitable for your use case and better suited to your specific needs. 

Equipping agents with necessary tools 

Next, you need to equip the agent with the necessary tools to carry out the tasks that the agent has been designed to fulfil.  

The agentic self-service tool hosts both domain-specific tools and generic tools.  

Domain tools are specific to use cases. For instance, ‘resumes screening RAG’ is an off-the-shelf tool for agents to screen resumes and ‘FinOps RAG connector’ is a ready-to-use tool that creates intuitive visualizations for FinOps costing. 

Generic tools provide the ability to perform more generic tasks such as read PDFs, Word files, or CSV [comma-separated values] files, search the web, send emails, scrape web pages, and more. 

Depending on the purpose and underlying ecosystem, agents may need to establish a connection with enterprise tools or applications and connect to external API or databases. For example, integrating with customer relationship management (CRM) systems illustrates how an agent can connect to internal databases to access the most relevant and up-to-date information. 

To further boost tool interoperability, you can connect with multiple tools hosted on Model Context Protocol (MCP) servers, allowing agents to seamlessly interact with external tools, APIs, and enterprise systems. For example, you can add Mistral’s OCR Tool to your toolkit via an MCP to read complex scanned PDFs. 

Orchestrating agentic workflows in AI 

Once the agent has the required tools, you need to define the task it needs to undertake. For example, you might instruct it to act as a tech expert that can simplify complex documents and communicate them clearly to a non-technical audience. This step involves specifying what the agent is supposed to do. 

Multi-agent AI systems involve creating several agents, each specializing in one or two tasks. It is important that all of these agents seamlessly collaborate to be able to execute complex workflows. 

The agentic self-service tool is built on a robust multi-agent framework that will enable you to design a workflow to orchestrate agent collaboration. For instance, you might have a mailbox agent to handle emails, a scheduler agent for orchestration, and a data-reader agent for summarizing documents. You can create simple workflows where each agent performs a task sequentially or more complex ones with multiple agents working in parallel. You can create an orchestrator agent that manages multiple worker agents, delegating tasks and ensuring everything runs smoothly. Alternatively, you can design workflows where agents collaborate autonomously to complete tasks without predefined sequences. 

Once your workflow is set up, you can visualize the workflow on a canvas in the most intuitive user experience before it is tested in a sandbox environment. This provides a picture of how the agents interact and perform their tasks.  

Taking the level of autonomy a step further, a plugged-in ‘Agent Builder’ is available to automatically build your agents and set up your workflows with a simple query in natural language. If you need to optimize your query, the Agent Builder comes with a ‘Prompt Optimizer’ which makes the overall interaction simple and effective. 

Managing economics with embedded analytics

You can also analyze the performance and cost of running the workflow, including token consumption and resource usage. This helps you determine if the workflow is cost-effective, manage economics and identify any areas for improvement. 

The agentic self-service tool is secured with guardrails and data compliance checks. The platform supports enterprise-grade authentication, security set-up, and audit trails, all in line with your enterprise governance standards. 

Creating and controlling agentic workflows

An agentic self-service tool simplifies the creation and management of multi-agent AI systems and agentic AI workflows. By following simple steps, you can build and refine your own agentic workflows, making it easier to automate and optimize various business processes. 

Whether you’re a tech expert or a non-technical user, an agentic self-service tool provides the flexibility and functionality you need to harness the power of agentic AI. 

References: 1. AI agents market size & trends, growth analysis, forecast [2030]